System Analysis and Operation Optimization for Aerospace and Related Manufacturing Processes

加拿大航空航天制造业的重要性怎么估计都不为过。根据2013年加拿大航空航天工业协会(AIAC)和加拿大工业新闻发布的数据，与该国其他制造业相比，加拿大航空航天工业对国家经济的总体影响、GDP总值的份额、研发活动、就业人数和质量等许多关键指标都排在非常高的位置，如果不是最高的话。先进的制造和组装技术，包括复合材料的制造和使用、精密加工、机器人和自动化等，在很大程度上促进了加拿大航空航天工业的成功。此外，各种系统工程方法和运营管理工具，如精益工程、供应链管理、生产计划、库存控制、质量控制和改进、制造单元配置等，应该也可以在航天制造过程的设计和改进中发挥更重要的作用。目前的文献和制造实践表明，在解决航空航天制造的不同制造问题时，需要进一步开发这些系统工程工具。例如，航空航天质量管理体系AS9100已被许多主要航空航天公司采用。然而，一些统计质量工具最初是为大规模生产系统开发的，例如汽车产品和消费品的系统。另一方面，航天制造通常与生产速度慢、产品种类多、质量要求非常高和生产成本高有关。标准统计工具和其他分析工具必须进一步发展或修订，才能适当地用于航空航天过程控制和优化。在拟议的研究中，我们将研究和开发统计质量工具，用于数据可获得性有限且不断发展的航空航天制造应用。此外，我们计划为航空航天供应链管理开发新的数学模型和求解方法，或修改现有的模型和方法，以考虑联合产品开发、利润和风险分担以及高水平的质量要求。我们还将研究与航天制造的最优生产计划和控制有关的问题。一个例子是将重型人工操作和机器人装配过程结合在一起的制造系统的生产控制，这些制造系统广泛存在于航空航天和其他制造业中。建议的研究成果将是适合于解决许多航空航天公司在供应链设计和管理、全面质量控制和改进以及最优制造系统和工艺开发方面面临的系统工程和优化问题的建模和解决方法。研究成果应有利于加拿大航空航天工业与其他发达国家/区域以及新兴经济体的航空航天工业竞争。

The importance of Canada's aerospace manufacturing sector cannot be overestimated in any terms. According to the 2013 Aerospace Industries Association of Canada (AIAC) and Industry Canada News Release, the overall impact of Canadian aerospace industry to the nation's economy, share of the total GDP value, R&D activities, employment numbers and quality, among many other key indicators, are all ranked very high, if not the top ones, in comparing to other manufacturing sectors in the country. Advanced fabrication and assembly technologies including the making and use of composite materials, precision machining, robotics and automation, among many others, have contributed to large extent to the success of Canadian aerospace industry. In addition, various system engineering methodologies and operation management tools such as lean engineering, supply chain management, production planning, inventory control, quality control and improvement, manufacturing cell configuration, etc., should and could play more significant roles in the design and improvement of aerospace manufacturing processes. The current literature and manufacturing practice show that there is a need to further develop these system engineering tools in addressing different manufacturing issues specific to aerospace manufacturing. For example, the aerospace quality management system, AS9100, has been adopted by many major aerospace companies. However, some of the statistical quality tools were initially developed for mass production systems such as those for automotive products and consumer goods. On the other hand, aerospace manufacturing is typically associated with slow production rate, large product variety, very high level quality requirements and high production cost. Standard statistical tools and other analytical tools must be further developed or revised before they can be properly used for aerospace process control and optimization. In the proposed research, we will investigate and develop statistical quality tools for applications in aerospace manufacturing where data availability is limited and evolving. In addition, we plan to develop new mathematical models and solution methods, or modify existing ones, for aerospace supply chain management considering joint product development, profit and risk sharing, and high level quality requirements. We will also investigate problems related to optimal production planning and control of aerospace manufacturing. An example problem is production control of manufacturing systems combining heavy manual operations and robotic assembly processes which widely exist in aerospace and other manufacturing industry. The outcome of the proposed research will be modeling and solution methodologies suitable for solving systems engineering and optimization problems many aerospace companies face in supply chain design and management, total quality control and improvement, and in optimal manufacturing system and process development. The research outcome should benefit Canadian aerospace industry in competing with those from other developed countries/regions as well as from emerging economies.